Eye Phone

EYEPHONE Emiliano Miluzzo, Tianyu Wang, Andrew T. Campbell, "EyePhone: Activating Mobile Phones With Your Eyes," In Proc. of the ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld'10), August 30, 2010 Class Code: EEL 6788 Date: 03-16-2011

Instructor: Dr. Damla Turgut

Presenter: Taranjeet Singh Bha

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Technology introduction HPI Evolution Terminologies Paper Abstract Eye Phone Design Evaluation Application Demo Future work Improvement on Paper Related work

Technology Introduction 

Human Computer Interaction(HCI) “HCI (human-computer interaction) is the study of how people interact with computers and to what extent computers are or are not developed for successful interaction with human beings”

Most HCI technology addresses the interaction between people and computers in “ideal” environments, i.e., where people sit in front of a desktop machine with specialized sensors and cameras centered on them

Technology Introduction 

Human Phone Interaction(HI) “Human-Computer Interaction (HCI) researchers and phone vendors are continuously searching for new approaches to reduce the effort users exert when accessing applications on limited form factor devices such as mobile phones.”

Human-phone interaction (HPI) extends the challenges not typically found in HCI research, more specially related to the phone and how we use it. In order to address these goals HPI technology should be less intrusive; that is, i) it should not rely on any external devices other than the mobile phone itself; ii) it should be readily usable with minimum user dependency as possible; iii) it should be fast in the inference phase; iv) it should be lightweight in terms of computation; v) it should preserve the phone user experience, e.g., it should not deplete the phone battery over normal operations.

HPI Evolution - Keypad 

1983 –

HPI Evolution – Touch Screen 

1993 –

HPI Evolution – Trackball 

2006 –

HPI Evolution – Voice Operated 

2010 –

HPI Evolution – What Next ?

Eye Phone – Controlling Phone by Eyes

Terminologies 

Intrusive Method:  which



requires direct contact with the eyes

Non- Intrusive methods:  which

avoid any physical contact with the user

 Model

based method  Appearance based method  Feature based method

Paper Abstract EyePhone tracks the user‟s eye movement across the phone‟s display using the camera mounted on the front of the phone i)

Track the eye and infer its position on the mobile phone display as a user views a particular application ii) Detect eye blinks that emulate mouse clicks to activate the target application under view.

EyePhone Design 1) An eye detection phase 2) An open eye template creation phase 3) An eye tracking phase 4) A blink detection phase

Eye detection phase 

By applying a motion analysis technique which operates on consecutive frames, this phase consists on finding the contour of the eyes. The eye pair is identified by the left and right eye contours.

Results of original algorithm running on a desk-top with a USB camera

Ref: Eye Tracking and Blink Detection Library.(http://tinyurl.com/yb9v4f2)

Results of EyePhone running on a Nokia N810. The smaller dots are erroneously interpreted as eye contours

Eye detection Based on previous experimental observations, they modify the original algorithm i)

Reducing the image resolution, which reduces the eye detection error rate

ii)

Adding two more criteria to the original heuristics that filter out the false eye contours widthmin ≤ width ≤ widthmax heightmin ≤ height ≤ heightmax

widthmin, widthmax, heightmin, heightmax are thresholds, which identify the possible sizes for a true eye contour, are determined under various experimental conditions (e.g., bright, dark, moving, not moving) and with different people. This design approach boosts the eye tracking accuracy considerably

Open eye template creation EyePhone creates a template of a user‟s open eye once at the beginning when a person uses the system for the first time using the eye detection algorithm. The template is saved in the persistent memory of the device and fetched when EyePhone is invoked 

Downside of this off-line template creation approach is that a template created in certain lighting conditions might not be perfectly suitable for other environments

Eye tracking The eye tracking algorithm is based on template matching. The template matching function calculates a correlation score between the open eye template, created the first time the application is used, and a search window.

Outer box around the left eye encloses the region where the correlation score is calculated. If the normalized correlation coefficient equals 0.4 we conclude that there is an eye in the search window. This threshold has been verified accurate by means of multiple experiments under different conditions (e.g., bright, dark, moving, not moving)

Eye tracking Correlation: 1.

2.

Correlation is a measure of the degree to which two variables agree, not necessary in actual value but in general behavior. The two variables are the corresponding pixel values in two images, template and source.

x is the template gray level image i 0 x is the average grey level in the template cor  N 1 N 1 2 xi  x     yi  y 2 image  y is the source image section i 0 i 0 y is the average grey level in the source image N is the number of pixels in the section image The value cor is between –1 and +1, with larger(N= valuestemplate representingimage a strongersize relationship between* the two = columns rows) N 1

images

( xi  x )   yi  y 

Blink Detection To detect blinks they apply a thresholding technique for the normalized correlation coefficient returned by the template matching function Problem: Quality of the mobile camera is not the same as a good USB camera, and the phone’s camera is generally closer to the person’s face than is the case of using a desktop and USB camera. Because of this latter situation the camera can pick up iris movements, i.e., the interior of the eye, due to eyeball rotation. In particular, when the iris is turned towards the corner of the eye, upwards or downwards, a blink is inferred even if the eye remains open. This occurs because in this case the majority of the eye ball surface turns white which is confused with the color of the skin Four thresholds: T1min = 0.64 T1max =0.75 T2min =-0.53 T2max =-0.45

If we indicate with cmin and cmax, respectively, the minimum and maximum normalized correlation coefficient values returned by the template matching function, the eye is inferred to be closed if T1min ≤ cmax ≤ T1max and T2min ≤ cmin ≤ T2max It is inferred to be open otherwise.

EVALUATION DS = eye tracking accuracy measured in daylight exposure and being steady; AS =eye tracking accuracy measured in artificial light exposure and being steady; DM = eye tracking accuracy measured in daylight exposure and walking; BD = blink detection accuracy in daylight exposure

Device Used: Nokia N810, 400MHz processor, 128 MB of RAM, Maemo 4.1 OS, OpenCV (Open Source Computer Vision) library, algorithms compiled on the Maemo scratchbox, video frames from the camera using GStreamer

EVALUATION Average CPU usage, RAM usage, and computation time for one video frame. The front camera supports up to 15 frames per second. The last column reports the percentage of used battery by EyePhone after a three hour run of the system

Applications 

EyeMenu

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Car Driver Safety

EyePhone could also be used to detect drivers drowsiness and distraction in cars.

Demo

http://sensorlab.cs.dartmouth.edu/eyephone/demo.m4 v

FUTURE WORK 

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Creation of the open eye template and the filtering algorithm for wrong eye contours Improvement over variations of lighting conditions or movement of the phone in a person‟s hand Using a learning approach instead of a fixed thresholding policy

Improvement On The Paper 

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Very Basic Approach used High speed and better DSP libraries are available Need to implement on I-phone and android phones Can be incorporated with face detection Iris tracking Improvement for various light condition and face orientation Online template creation

RELATED WORK There are a number of developments in HCI related to mobile phones over the last several years  Use of accelerometers on the phone in order to infer gestures  PhonePoint Pen project : phone being transformed into a pen to write in the air in order to quickly take small notes without needing to either write on paper or type on a computer. [4]  uWave project exploits a 3-D accelerometer on a Wii remote-based prototype for personalized gesture recognition. [7]  Eye movement has recently been used for activity recognition. By tracking the eye researchers show that it is possible to infer the actions of watching a video, browsing the web or reading notes. [24]  The eyeSight technology exploits the phone camera to control the mobile phone by simple hand gestures over the camera. [5]

PAPER REFERENCES [4] S. Agrawal, I. Constandache, S. Gaonkar, and R.R.Choudhury. PhonePoint Pen: Using Mobile Phones to Write in Air. In MobiHeld „09,pages1–6.ACM,2009. [5] eyeSight. http://www.eyesight-tech.com [7] J. Liu, L. Zhong, J. Wickramasuriya, and V. Vasudevan. uWave: Accelerometer-Based Personalized Gesture Recognition and its Applications. In Pervasive and Mobile Computing, vol. 5, issue 6, pp. 657-675, December 2009. [8] Accelerometer-Based Gesture Recognition with the iPhone. http://tinyurl.com/yjddr8q. [9] The Mobile Phone that Could “Read Lips”. http://www.cellular-news.com/story/42211.php. 10] Nokia Mobile Radar. http://tinyurl.com/yfo2dtd. [11] The Eye Mouse project. http://www.arts.ac.uk/research/eyemouse/index.htm. [12] F. Karray, M. Alemzadeh, J.A. Saleh, and M.N. Arab.Human-Computer Interaction: Overview on State of the Art. In International Journal on Smart Sensing and Intelligent Systems, Vol. 1, No. 1, March 2008. [13] M. Chau and M. Betke. Real Time Eye Tracking and Blink Detection with USB Cameras. In Boston University Computer Science Technical Report No.2005-12,2005. [14] E. Miluzzo, C.T. Cornelius, A. Ramaswamy,T. Choudhury, Z. Liu, A.T. Campbell. Darwin Phones: The Evolution of Sensing and Inference on Mobile Phones. In Eighth International ACM Conference on Mobile Systems, Applications, and Services (MobiSys ‟10), San Francisco, CA, USA, June 15-18, 2010. [15] EyePhone demo video. http://sensorlab.cs.dartmouth.edu/eyephone/demo.m4v [16] Nokia n900. http://maemo.nokia.com/n900. [17] Eye Tracking and Blink Detection Library. http://tinyurl.com/yb9v4f2.

Thank You

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